Package 'Thermimage'

Description

Assists in converting raw thermal imaging data files into temperature values.

Details

Primary purpose of the package is to assist with manipulating raw data extracted from thermal image files. These raw data are stored in a raw data format and require inforamtion about various environmental variables to estimate surface temperatures accurately. raw2temp is the primary function of use. Other functions included involve simple scripts for data handling.

Author(s)

Glenn J. Tattersall

Please report issues, upload problems, or provide sample files to the following site: https://github.com/gtatters/Thermimage/issues

References

1. http://130.15.24.88/exiftool/forum/index.php/topic,4898.60.html

2. Minkina, W. and Dudzik, S. 2009. Infrared Thermography: Errors and Uncertainties. Wiley Press, 192 pp.

Description

Density of air if temperature (degrees Celsius) provided. Units: kg/m3

Usage

airdensity(Ta = 20)

Arguments

Author(s)

Glenn J Tattersall

References

http://www.engineeringtoolbox.com/air-properties-d_156.html

Examples

## The function is currently defined as
function (Ta = 20) 
{
    Base <- 314.156
    Exponent <- (-0.981)
    p <- Base * (Ta + 273.15)^Exponent
    p
  }

Description

Specific heat capacity of air if temperature (degrees Celsius) provided. Units: J/(kg*K)

Usage

airspecificheat(Ta = 20)

Arguments

Author(s)

Glenn J Tattersall

References

http://www.engineeringtoolbox.com/air-properties-d_156.html

Examples

## The function is currently defined as
function (Ta = 20) 
{
    Intercept <- 1.003731424
    Slope1 <- 5.37909e-06
    Slope2 <- 7.30124e-07
    Slope3 <- (-1.34472e-09)
    Slope4 <- 1.23027e-12
    cp <- 1000*(Intercept + Slope1 * Ta + Slope2 * Ta^2 + Slope3 * 
        Ta^3 + Slope4 * Ta^4)
    cp
  }

Description

Thermal conductivity of air. Units: W/m/K

Usage

airtconductivity(Ta = 20)

Arguments

Author(s)

Glenn J Tattersall

References

http://www.engineeringtoolbox.com/air-properties-d_156.html

See Also

airviscosity

Examples

## The function is currently defined as
function (Ta = 20) 
{
    Intercept <- 0.024280952
    Slope <- 7.07143e-05
    k <- Intercept + Slope * Ta
    k
  }
# Example calculation:
Ta<-20
airtconductivity(Ta)

Description

Returns the air viscosity value for a given, supplied air temperature (Ta). Ta should be in units of oC.

Usage

airviscosity(Ta = 20)

Arguments

Value

Kinematic viscosity of air, as a function of temperature Units: m2/s Regression for 0 to 100oC range: Intercept<-13.17380952 Slope<-0.097457143 k<-(Intercept+Slope*Ta)*1e-6 # multiply by 1e-6 to get into m2/s units

Author(s)

Glenn J Tattersall

References

http://www.engineeringtoolbox.com/air-properties-d_156.html

Examples

## The function is currently defined as
function (Ta = 20) 
{
    Intercept <- 13.17380952
    Slope <- 0.097457143
    k <- (Intercept + Slope * Ta) * 1e-06
    k
  }
# Example calculation
Ta<-20
airviscosity(Ta)

Description

Provides the surface area of a cone with an elliptical base. For a circular cone, simply use Radius=radius.

Usage

areacone(Radius, radius=Radius, hypotenuse=NULL, height, ends=1)

Arguments

Details

Calculates the surface are of a cone with an elliptical base.

Author(s)

Glenn J Tattersall

Examples

## The function is currently defined as
function(Radius, radius=Radius, hypotenuse=NULL, height, ends=1)
{
  if(is.null(hypotenuse)){
    hypotenuse<-sqrt(height^2+Radius^2)
  }
  Area <- ends*pi*Radius*radius + pi*Radius*hypotenuse
  Area
}

# Example calculation from a measure of a bird bill.

# Typically, a bird bill will be measured by its depth (d) at the base, its width (w) at the 
# base and by its overall length.  The length (l) is typically measured along the length of
# the culmen, and thus is a diagonal measure along the hypotenuse of the cone.

d<-12
w<-6
l<-18
areacone(Radius=d/2, radius=w/2, hypotenuse=l, height=NULL, ends=1)


# If the perpendicular cone height (h) is instead measured, rather than the hypotenuse, then
# substitute h for height and assign hypotenuse = NULL, to obtain the same result

h<-sqrt(l^2-(d/2)^2)
areacone(Radius=d/2, radius=w/2, hypotenuse=NULL, height=h, ends=1)


# To only show surface area of the exposed surface, and exclude the oval base of the cone
# set ends=0:

areacone(Radius=d/2, radius=w/2, hypotenuse=l/2, height=NULL, ends=0)

Description

Provides the surface area of a cylinder, including the circular bases.

Usage

areacylinder(Radius, radius=Radius, height, ends = 2)

Arguments

Author(s)

Glenn J Tattersall

Examples

## The function is currently defined as
function(Radius, radius=Radius, height, ends=2)
{
  Area <- (Radius+radius)*pi*height + ends*pi*Radius*radius
  Area
  }

# Example calculation:

# Typically, a body part might be modelled as cylindrical if it appears to be approximately
# circular or elliptical and elongated.  By measuring the major diameter (D) and minor
# diameter (d) as well as the length or height (l), the overall surface area can be 
# determined:

D<-12
d<-6
l<-18
areacylinder(Radius=D/2, radius=d/2, height=l, ends=2)


# To only show surface area of the exposed surface, and exclude the oval base of the 
# cylinder, set ends=0

areacylinder(Radius=D/2, radius=d/2, height=l, ends=0)

Description

Provides the surface area of a sphere.

Usage

areasphere(radius)

Arguments

Author(s)

Glenn J Tattersall

Examples

## The function is currently defined as
function (radius) 
{
    Area <- 4 * pi * radius^2
    Area
  }

# Example calculation:
radius<-4
areasphere(radius)

Description

Invoking shell commands to act on a FLIR jpg and calls the exiftool -RawThermalImage option to extract the raw, binary thermal image data in 16 bit format and passes this to imagemagick's convert function to swap the byte order (if necessary) and output as a png file.

Usage

convertflirJPG(imagefile, exiftoolpath="installed", res.in="640x480", 
endian="lsb", outputfolder="output", verbose=FALSE, ...)

Arguments

Details

Calls exiftool and imagemagick (convert) in shell to convert a FLIR jpg, using the command line exiftool, and passing that raw thermal binary datat to convert to create a png file. The subsequent converted file is a 16 bit grayscale png, with each pixel representing the uncalibrated raw sensor radiance data from the thermal imaging camera. This raw png file can be loaded into ImageJ for further analysis.

For example, a typical shell call might look like:

exiftool FLIRjpgfilename.jpg -b -RawThermalImage | convert - gray:- | convert -depth 16 -endian lsb -size 640x480 gray:- Outputfilename.png

Value

No output generated in R. Shell call to exiftool and imagemagick to convert flir jpg files to png files. exiftool and imagemagick must be installed on the system. Files generated require further processing to estimate temperature.

Note

This function has not been fully tested with all flir jpg types. Multiburst images and older camera file types may not work.

This function requires that exiftool and imagemagick are installed. Consult with the references for how to install

Author(s)

Glenn J. Tattersall

References

1. https://www.sno.phy.queensu.ca/~phil/exiftool/

2. https://www.imagemagick.org/script/index.php

See Also

convertflirVID, ffmpegcall, readflirJPG,

Examples

# Based on the following command line unix code, 
# this function will convert a flir jpg into a 16 bit 
# greyscale png to import into imageJ

# Equivalent command line code:
# exiftool FLIRjpgfilename.jpg -b -RawThermalImage | convert - gray:- | 
# convert -depth 16 -endian lsb 
# -size 640x480 gray:- Outputfilename.png

# Examples
# See https://github.com/gtatters/FLIRJPGConvert/blob/master/Examples.R

# See https://github.com/gtatters/FLIRJPGConvert/blob/master/FLIRJPG_Convert.R 

# See https://github.com/gtatters/Thermimage/blob/master/README.md

Description

Invoking shell commands to act on a FLIR video (SEQ or CSQ file type) and calls the exiftool -RawThermalImage option to extract the raw, binary thermal image frames in 16 bit format and pass these to ffmpeg to convert the output as a series of png files or as an avi video file.

Usage

convertflirVID(imagefile, exiftoolpath="installed", perlpath="installed", 
fffsplitpattern="fff", fr=30, res.in="1024x768", res.out="1024x768", 
outputcompresstype="jpegls", outputfilenameroot=NULL, outputfiletype="avi",
outputfolder="output", verbose=FALSE,...)

Arguments

Details

Calls exiftool, imagemagick, and ffmpeg in shell to convert a thermal image video file (SEQ or CSQ) into a 16 bit grayscale avi or series of images corresponding to each frame of the input video.

Value

No output generated in R. Shell call to exiftool, imagemagick, and ffmpeg to convert files.

Note

Use with files <2Gb in size. Larger files have failed during testing due to internal memory limits during call to perl.

This function requires that exiftool and ffmpeg are installed. Consult with the references for how to install.

Author(s)

Glenn J. Tattersall

References

1. https://www.sno.phy.queensu.ca/~phil/exiftool/

2. https://www.imagemagick.org/script/index.php

3. https://www.eevblog.com/forum/thermal-imaging/csq-file-format/

See Also

convertflirJPG, ffmpegcall, readflirJPG,

Examples

# Based on the following command line unix code, this function will convert a 
# flir jpg into a 16 bit greyscale video or sequence of images for import into imageJ

# Equivalent command line code:
# ffmpeg -f image2 -vcodec tiff -r 30 -s 640x480 -i 'output/frame%05d.tiff' -pix_fmt gray16be 
# -vcodec png -s 640x480 file.avi

# Examples 
# See https://github.com/gtatters/FLIRJPGConvert/blob/master/Examples.R

# See https://github.com/gtatters/FLIRJPGConvert/blob/master/FLIRJPG_Convert.R 

# See https://github.com/gtatters/Thermimage/blob/master/README.md

Description

Based on the absolute difference sum method (Lighton and Turner, 2004), this function takes a difference frame dataframe, where each column corresponds to a video frame (i+1) that has been subtracted from the previous (ith) frame. Each row corresponds to a pixel difference value.

Usage

cumulDiff(fdiff, extract.times, samples = 2)

Arguments

Details

Each row in fdiff corresponds to a specific pixel position in a thermal video frame. Data frames are preferred over array functions for speed and simplicity. Row numbers range from 1 through to the image dimensions (i.e. w*h = 640 * 480=307200). Image dimensions are not required, provided the row number corresponds to the same relative position.

The premise behind this is that the thermal video is either time lapse or higher speed video. If a specific pixel shows no change (0) from frame to frame, then there is no movement or temperature change. For videos of living specimens, movement artefacts will manifest as change over time at specific pixels. If there is sufficient movement, across the image space, the accumulation of small differences will provide a measure of relative activity from frame to frame.

cumulDiff takes the average, standard deviation and rootmean square of all pixels within one frame to arrive at an aggregate value for each difference frame (absolute value). Subsequently, it sums these successive data points (avg,sd,rms) across all frames, arriving at an absolute difference summation. This results in an incrementing value, of which the slope will be a semi-quantitative assessment of relative change. It also provides a clean break point when activity ceases (Lighton, 2008).

The extract.times value (POSIX) is required to provide a time index as well as to calculate the frame rate.

Value

Returns a list variable, containing raw, cumulative difference calculations and the slope calculations on a minimum of 2, preferrably every 3rd frame.

Author(s)

Glenn J Tattersall

References

1. Lighton, J.R.B., and Turner, R.J. (2004). Thermolimit respirometry: an objective assessment of critical thermal maxima in two sympatric desert harvester ants, Pogonomyrmex rugosus and P. californicus. J Exp Biol 207: 1903-1913.

2. Lighton, J. R. B. (2008). Measuring metabolic rates : a manual for scientists. Oxford ; New York, Oxford University Press.

See Also

diffFrame

Examples

# Create a vector of arbitrary frame times - these would be extracted normally using the 
# locateFrames and getTimes functions

start<-as.POSIXct("2017-03-31 12:00:00")
fdiff<-data.frame(matrix(runif(307200*20, 20, 40), nrow=307200))

# add noise to pixels
for(i in 1:20){
  randpixels<-floor(runif(10000, 1,307200))
  fdiff[randpixels,i]<-fdiff[randpixels,i]*runif(1, 10, 10000)
}

extract.times<-seq(start, start+20,1)
cumulDiff(fdiff, extract.times, 2)

Description

Works similarly to the simple diff() function, but on a data.frame. Subtracts column i from column i+1, assuming each column represents the pixel information for one frame of an imported thermal image video. Each row in the column corresponds to a pixel. Returns a data.frame of one column shorter dimension than the original data.frame.

Usage

diffFrame(dat, absolute = TRUE)

Arguments

Details

Providing a data frame of R x C dimensions, returns a data frame of R x (C-1) dimensions, where each column represents the difference between adjacent columns. Absolute or relative values are provided.

Each row in dat corresponds to a specific pixel position in a thermal video frame. Data frames are preferred over array functions for speed and simplicity. Row numbers range from 1 through to the image dimensions (i.e. w*h = 640 * 480=307200).

The premise behind this is that the thermal video is either time lapse or higher speed video. If a specific pixel shows no change (0) from frame to frame, then there is no movement or temperature change. For videos of living specimens, movement artefacts will manifest as change over time at specific pixels. If there is sufficient movement, across the image space, the accumulation of small differences will provide a measure of relative activity from frame to frame.

In combination of a cumulative summation function (cumulDiff), the diffFrame function can assess relative change in movement or activity. This makes use of a concept called the absolute difference sum method, sometimes used to simplify noisy data. See cumulDiff for further info.

Value

Returns a data frame of R x (C-1) dimensions, where each column represents the difference between adjacent columns.

Author(s)

Glenn J Tattersall

References

1. Lighton, J.R.B., and Turner, R.J. (2004). Thermolimit respirometry: an objective assessment of critical thermal maxima in two sympatric desert harvester ants, Pogonomyrmex rugosus and P. californicus. J Exp Biol 207: 1903-1913.

2. Lighton, J. R. B. (2008). Measuring metabolic rates : a manual for scientists. Oxford ; New York, Oxford University Press.

See Also

cumulDiff

Examples

# set w to 640 and h to 480
w<-640
h<-480
f<-system.file("extdata", "SampleSEQ.seq", package = "Thermimage")
x<-frameLocates(f, w=w, h=h)
suppressWarnings(templookup<-raw2temp(1:65535))
alldata<-unlist(lapply(x$f.start, getFrames, vidfile=f, w=w, h=h))
alldata<-matrix(alldata, nrow=w*h, byrow=FALSE)
alltemperature<-templookup[alldata]
alltemperature<-unname(matrix(alltemperature, nrow=w*h, byrow=FALSE))

dalltemperature<-as.matrix(diffFrame(alltemperature, absolute=TRUE), nrow=w)

# Plot 
plotTherm(dalltemperature[,1], templookup=NULL, w=w, h=h,  minrangeset=min(dalltemperature),
          maxrangeset=max(dalltemperature), trans="mirror.matrix")

Description

A simplified wrapper function calling ffmpeg to convert numbered files extracted from FLIR thermal image videos via exiftool into radiometric png files or radiometric avi files. Mostly for internal use.

Usage

ffmpegcall(filenameroot, filenamesuffix="%05d", filenameext="jpegls", 
incompresstype="jpegls", fr=30, res.in="640x480", res.out=res.in, 
outputcompresstype="png", outputfilenameroot=NULL, outputfiletype="avi", 
outputfolder="output",...)

Arguments

Details

Calls ffmpeg in shell to convert a series of image files, named filenameroot%05d.filenameext, extracted from a thermal image file using the command line tool, exiftool. The subsequent converted file is a 16 bit grayscale avi or series of images corresponding to each of the input files.

For example, a typical shell call to ffmpeg might look like:

ffmpeg -f image2 -vcodec fff -i frame%05d.fff -f image2 -vcodec png frame%05d.png -y

which converts a series of fff files (frameNNNNN.fff) into a series of png files (frameNNNNN.png).

Likewise, the following:

ffmpeg -r 30 -f image2 -vcodec jpegls -s 1024x768 -i frame%05d.jpegls -vcodec png -s 1024x768 frame.avi -y

converts a series of jpegls files (frameNNNNN.jpegls) into an avi file (frame.avi) with png style compression

Jpeg-ls is a lossless jpg format (JPG-LS) that is used for certain flir image types (e.g., CSQ, Ultramax FLIR jpg). The easiest means to convert the extracted, compressed data type is with ffmpeg, which contains the codecs for extraction.

For example, once ffmpeg is installed, try in shell:

ffmpeg -codecs | grep jpegls

Value

No output generated in R. Shell call to ffmpeg to convert files. ffmpeg must be installed on the system.

Author(s)

Glenn J. Tattersall

References

1. https://www.ffmpeg.org/

2. https://www.eevblog.com/forum/thermal-imaging/csq-file-format/

3. http://www.digitalpreservation.gov/formats/fdd/fdd000151.shtml

See Also

convertflirVID, convertflirJPG

Examples

# Examples 
# See https://github.com/gtatters/FLIRJPGConvert/blob/master/Examples.R

# See https://github.com/gtatters/Thermimage/blob/master/README.md

Description

Flips a matrix 'left-right'. Used in re-arranging image data for plotting properly in R.

Usage

flip.matrix(x)

Arguments

Author(s)

Glenn J Tattersall

References

1. http://www.inside-r.org/packages/cran/RSEIS/docs/mirror.matrix

2. Based on similar code in package <RSEIS>

See Also

mirror.matrix rotate90.matrix rotate270.matrix rotate180.matrix

Examples

## The function is currently defined as
function (x) 
{
    mirror.matrix(rotate180.matrix(x))
  }


par(mfrow=c(1,2),mar=c(1,1,1,1))
r<-c(1:100,rnorm(1:100)*10,1:100)
m<-matrix(r,20)
image(m, axes=FALSE)
box()
text(.5,.5,"Matrix",col="white")
mf<-flip.matrix(m)
image(mf,axes=FALSE)
box()
text(.5,.5,"Flipped",col="white")

Description

A text file containing the palette information for use in thermal images

Description

Extracts meta tag information from a FLIR JPG image using system installed Exiftool application. Use this to obtain thermal image calibration values, date/time stamps, object distance, and other parameters saved in FLIR image or video files.

Usage

flirsettings(imagefile, exiftoolpath = "installed", camvals = NULL)

Arguments

Details

The imagefile should be the original captured FLIR JPG file, not a modified JPG. This also works with FLIR video files (.seq and .fcf).

Exiftool should install on most operating systems. Consult with http://www.sno.phy.queensu.ca/~phil/exiftool/ for information on installing Exiftool. If trouble installing, download Exiftool perl scripts and set exiftoolpath to the custom folder locationto access the perl scripts that are attached with this package.

For camvals, provide a character string as described in Exiftool documentation. Set camvals="-*Emissivity", to simply return the Emissivity value. Set camvals="-*Planck*" for camera calibration constants.

Note: the Emissivity value is simply that which is stored in the file. It typically is the default value the camera is set to (0.95), but this does not mean that the true Emissivity of the surface is what is stored in the file. Similar caution is advised regarding the environmental parameters returned from the meta tags. User knowledge is required.

Value

Returns a list of camera meta tags for use in thermal imaging calculations.

Info is the basic list of camera settings.

Dates will be the date values associated with the image creation, modification etc.

Note

Requires Exiftool be installed. see http://www.sno.phy.queensu.ca/~phil/exiftool/

Author(s)

Glenn J Tattersall

References

1. http://www.sno.phy.queensu.ca/~phil/exiftool/ 2. http://www.sno.phy.queensu.ca/~phil/exiftool/TagNames/FLIR.html

Examples

## Not run: 
## To access meta-tag information from a flir jpg or flir file:

## Example using the flirsettings functions:

library(Thermimage)
## Sample flir jpg included with Thermimage package:

imagefile<-paste0(system.file("extdata/IR_2412.jpg", package="Thermimage"))

## Extract meta-tags from thermal image file ##
cams<-flirsettings(imagefile, exiftool="installed", camvals="")
cams

## Set variables for calculation of temperature values from raw A/D sensor data
Emissivity<-cams$Info$Emissivity      # Image Saved Emissivity - should be ~0.95 or 0.96
ObjectEmissivity<-0.96                # Object Emissivity - should be ~0.95 or 0.96
dateOriginal<-cams$Dates$DateTimeOriginal
dateModif<-   cams$Dates$FileModificationDateTime
PlanckR1<-    cams$Info$PlanckR1                      # Planck R1 constant for camera  
PlanckB<-     cams$Info$PlanckB                       # Planck B constant for camera  
PlanckF<-     cams$Info$PlanckF                       # Planck F constant for camera
PlanckO<-     cams$Info$PlanckO                       # Planck O constant for camera
PlanckR2<-    cams$Info$PlanckR2                      # Planck R2 constant for camera
ATA1<-        cams$Info$AtmosphericTransAlpha1        # Atmospheric attenuation constant
ATA2<-        cams$Info$AtmosphericTransAlpha2        # Atmospheric attenuation constant
ATB1<-        cams$Info$AtmosphericTransBeta1         # Atmospheric attenuation constant
ATB2<-        cams$Info$AtmosphericTransBeta2         # Atmospheric attenuation constant
ATX<-         cams$Info$AtmosphericTransX             # Atmospheric attenuation constant
OD<-          cams$Info$ObjectDistance                # object distance in metres
FD<-          cams$Info$FocusDistance                 # focus distance in metres
ReflT<-       cams$Info$ReflectedApparentTemperature  # Reflected apparent temperature
AtmosT<-      cams$Info$AtmosphericTemperature        # Atmospheric temperature
IRWinT<-      cams$Info$IRWindowTemperature           # IR Window Temperature
IRWinTran<-   cams$Info$IRWindowTransmission          # IR Window transparency
RH<-          cams$Info$RelativeHumidity              # Relative Humidity
h<-           cams$Info$RawThermalImageHeight         # sensor height (i.e. image height)
w<-           cams$Info$RawThermalImageWidth          # sensor width (i.e. image width)

## See also https://github.com/gtatters/Thermimage/README.md

## End(Not run)

Description

Parameters required for forced convection equation and heat exchange estimation.

Usage

forcedparameters(V = 1, L = 0.1, Ta = 20, shape = "hcylinder")

Arguments

Details

Gates (2003) describes coefficients that characterise the base and exponent values used to calculate Nusselt numbers from Reynolds number as: c*Re^n. This function will return those parameters.

Value

A vector of length two, with values c and n.

Author(s)

Glenn J Tattersall

References

Blaxter, 1986. Energy metabolism in animals and man. Cambridge University Press, Cambridge, UK, 340 pp.

Gates, DM. 2003. Biophysical Ecology. Dover Publications, Mineola, New York, 611 pp.

See Also

freeparameters Nusseltforced

Examples

## The function is currently defined as
function (V = 1, L = 0.1, Ta = 20, shape = "hcylinder") 
{
    Re <- Reynolds(V, L, airviscosity(Ta))
    if (shape == "vplate" | shape == "hplate") 
        shape <- "plate"
    if (shape == "vcylinder" | shape == "hcylinder") 
        shape <- "cylinder"
    if (shape == "plate") {
        c = 0.595
        n = 0.5
    }
    if (shape == "sphere") {
        c = 0.37
        n = 0.6
    }
    if (shape == "cylinder" & Re >= 0.4 & Re < 4) {
        c <- 0.891
        n = 0.33
    }
    if (shape == "cylinder" & Re >= 4 & Re < 40) {
        c <- 0.821
        n = 0.385
    }
    if (shape == "cylinder" & Re >= 40 & Re < 4000) {
        c <- 0.615
        n = 0.466
    }
    if (shape == "cylinder" & Re >= 4000 & Re < 40000) {
        c <- 0.174
        n = 0.618
    }
    if (shape == "cylinder" & Re >= 40000 & Re < 4e+05) {
        c <- 0.024
        n = 0.805
    }
    coeffs <- c(c, n)
    names(coeffs) <- c("c", "n")
    coeffs
  }
  # Example:
V<-1
L<-0.1
Ta<-20
shape="hcylinder"
forcedparameters(V, L, Ta, shape)

shape="vcylinder"
forcedparameters(V, L, Ta, shape)

shape="hplate"
forcedparameters(V, L, Ta, shape)

shape="vplate"
forcedparameters(V, L, Ta, shape)

shape="sphere"
forcedparameters(V, L, Ta, shape)

Description

Using readBin function, find everywhere in file where the magic-byte/thermal resolution info is stored: i.e. 640x480, 320x240. These positions denote where the image frame data is found in the larger video file and will facilitate extraction of image save times and pixel information.

Usage

frameLocates(vidfile = "", w = 640, h = 480)

Arguments

Details

FLIR cameras have built-in radiometric video saving functions. FLIR software also has similar video, or time lapse, functionality. These files are typically stored as .seq or .fcf and encode information on the thermal imaging camera model, calibration, date/time, etc. These meta-tags can be extracted using system installed software (Exiftool).

This function makes use of the readBin function in the R base package, by loading a small portion of the file in raw(). It then searches through this data vector for the magic byte sequence in hexadecimal (0200wwwwhhhh) where wwww is the image width in little endian hexadecimal, and hhhh is the image height in little endian hexademical.

The actual start of all the magic byte locations is empirically determined by the repeating pattern of locations within the file.

Frame refers to the still frame that is to be extracted from the thermal video file.

The function returns a list, containing the 'header' start (h.start) position of each frame and the 'frame' start (f.start) where pixel data is stored in raw, binary format (at present, in 16-Bit integers).

h.start and f.start can be passed to other functions to extract the precise times of each frame (getTimes) and to extract the actual frame by frame data (getFrames).

The length of h.start and f.start should be the same. If these are blank, then the detection process has not worked and the filetype might not be supported by this function.

Warning: this is not tested on all samples of all video file types and may return errors for .fcf files.

Value

Returns a list, containing two vectors, h.start and f.start. These should be the same length.

Note

Requires Exiftool be installed in order to automatically determine thermal image width and height. If you know the width and height in pixels, then the frame start locations can be determined.

For information on installing Exiftool, see http://www.sno.phy.queensu.ca/~phil/exiftool/

Author(s)

Glenn J Tattersall

References

1. http://www.sno.phy.queensu.ca/~phil/exiftool/ 2. http://www.sno.phy.queensu.ca/~phil/exiftool/TagNames/FLIR.html

See Also

getFrames, getTimes, readBin

Examples

x<-frameLocates(vidfile = system.file("extdata", "SampleSEQ.seq", package = "Thermimage"))
x$h.start
x$f.start

Description

Parameters required for free convection equation and heat exchange estimation.

Usage

freeparameters(L = 0.1, Ts = 30, Ta = 20, shape = "hcylinder")

Arguments

Details

Gates (2003) describes coefficients that characterise laminar flow patterns describing how to calculate Nusselt numbers for objects of different shapes. This function will return those parameters. At present, it only supplies coefficients for different shapes, not for laminar vs. turbulent since free convection is not often used in biological applications.

Value

A vector of length three, with values a, b, and m.

Author(s)

Glenn J Tattersall

References

Blaxter, 1986. Energy metabolism in animals and man. Cambridge University Press, Cambridge, UK, 340 pp.

Gates, DM. 2003. Biophysical Ecology. Dover Publications, Mineola, New York, 611 pp.

See Also

Nusseltfree forcedparameters

Examples

## The function is currently defined as
function (L = 0.1, Ts = 30, Ta = 20, shape = "hcylinder") 
{
    a = 1
    Gr <- Grashof(L = 1, Ts = Ts, Ta = Ta)
    Pr <- Prandtl(Ta)
    if (shape == "hcylinder") {
        b <- 0.53
        m <- 0.25
    }
    if (shape == "vcylinder") {
        b <- 0.726
        m <- 0.25
    }
    if (shape == "hplate") {
        b <- 0.71
        m <- 0.25
    }
    if (shape == "vplate") {
        b <- 0.523
        m <- 0.25
    }
    if (shape == "sphere") {
        b <- 0.58
        m <- 0.25
    }
    coeffs <- c(a, b, m)
    names(coeffs) <- c("a", "b", "m")
    coeffs
  }

# Example:
L<-0.1
Ts<-30
Ta<-20
shape="hcylinder"
freeparameters(L, Ts, Ta, shape)

shape="vcylinder"
freeparameters(L, Ts, Ta, shape)

shape="hplate"
freeparameters(L, Ts, Ta, shape)

shape="vplate"
freeparameters(L, Ts, Ta, shape)

shape="sphere"
freeparameters(L, Ts, Ta, shape)

Description

Extracts raw binary thermal image data in integer format as a vector from a flir seq file.

Usage

getFrames(vidfile, framestarts, w = 640, h = 480, l = w * h, byte.length = 2, 
reverse=FALSE, magic2pixel=32)

Arguments

Details

This function will load into memory the raw binary pixel data from the entire thermal video file. Data are stored as read in using the readBin function, but the number of frames read in can be determined by dividing the length of the vector by (w*h*byte.length). Depending on the size of the video, this can become quite large.

Frame data is stored as a vector to speed calculations. Thermal video files may exceed memory capacity of some systems, so processing as arrays or dataframes is generally avoided.

As written, this is a vectorised function, so will only load in one frame is used normally. To load multiple frames from the video file, use a for-loop (usually slow) or the apply function to import (faster processing) or parallel apply functions (best).

Value

Returns a vector of integers, each item corresponding to raw pixel value. With information on thermal image width and height, the specific image can be reconstructed. To be used in conjunction with raw2temp function which will convert this raw binary value into an estimated temperature.

Note

Requires Exiftool be installed in order to automatically determine thermal image width and height. If you know the width and height in pixels, then the frame start locations can be determined.

For information on installing Exiftool, see http://www.sno.phy.queensu.ca/~phil/exiftool/

See convertflirVID function for an alternative to getFrames. The latter is loaded into R, which has high processor requirements. It is likely more feasible to first convert the thermal video into a format to be imported into an image stack processing program like ImageJ.

Author(s)

Glenn J Tattersall

References

1. http://www.sno.phy.queensu.ca/~phil/exiftool/ 2. http://www.sno.phy.queensu.ca/~phil/exiftool/TagNames/FLIR.html

See Also

frameLocates, getTimes, readBin, raw2temp, convertflirVID

Examples

# set w to 640 and h to 480

w<-640
h<-480
f<-system.file("extdata", "SampleSEQ.seq", package = "Thermimage")
x<-frameLocates(f, w=w, h=h)

# Slow approach:
system.time({
  alldata<-matrix(nrow=w*h, ncol=length(x$f.start))
  for(i in 1:length(x$f.start)) alldata[,i]<-getFrames(f, x$f.start[i], w, h)
})
dim(alldata)

# Faster approach
alldata<-NULL
system.time(alldata<-unlist(lapply(x$f.start, getFrames, vidfile=f, w=w, h=h)))
length(alldata)/(w*h)

## Parallel approach (requires parallel package. will not be faster on small files)
# library(parallel)
# alldata<-NULL
## set mc.cores to higher number to use parallel processing
# system.time(alldata<-unlist(mclapply(x$f.start, getFrames, vidfile=f, mc.cores=1)))
# length(alldata)/(w*h) # number of frames in video

Description

Extracts time values for each image frame from a thermal camera video file (.seq or with some .fcf). For time lapse or video capture, computer time is stored for each image frame in 3 chunks, denoting msec, sec, and date information.

Usage

getTimes(vidfile, headstarts, timestart = 900, byte.length = 1)

Arguments

Details

Somewhat empirically determined, but also information provided on the exiftool website below describes where time stamp information is stored in each file. This function concatentates the 3 time stamps corresponding to msec, sec, and date into one variable that gives the actual time each image was captured.

As written, this is a vectorised function, so to extract multiple frames of data (i.e. length(headstarts)>1), use a loop or the apply function as shown in the example below.

Extracted times are used in sumamrising information about the temperature profiles of the thermal videos and can be passed to the cumulDiff function.

Extracted times can also be used to verify the frame rate of the image capture in the video.

Has not been fully tested on file types from all cameras or thermal imaging software.

Value

Returns a vector of times as characters corresponding to the frame capture times as extracted from the thermal video file. Times should resemble those returned using Exiftool.

Author(s)

Glenn J Tattersall

References

1. http://www.sno.phy.queensu.ca/~phil/exiftool/

2. http://www.sno.phy.queensu.ca/~phil/exiftool/TagNames/FLIR.html

3. http://www.silisoftware.com/tools/date.php

4. http://www.sandersonforensics.com/forum/content.php?131-A-brief-history-of-time-stamps

See Also

getFrames, frameLocates, cumulDiff

Examples

w=640
h=480
f<-system.file("extdata", "SampleSEQ.seq", package = "Thermimage")
x<-frameLocates(f, w=w, h=h)
getTimes(f, x$h.start)

# only returns the first frame of data, must use lapply to get all frames

# Using lapply
extract.times<-do.call("c", lapply(x$h.start, getTimes, vidfile=f))
extract.times

# Using parallel lapply (uncomment below):
# library(parallel)
## set mc.cores to higher number to use parallel processing:
# extract.times<-do.call("c", mclapply(x$h.start, getTimes, vidfile=f, mc.cores=1))
# extract.times

Description

A text file containing the palette information for use in thermal images

Description

Determines the Grashof number for an object. The Grashof number is used in calculations of heat exchange.

Usage

Grashof(L = 1, Ts = 25, Ta = 20)

Arguments

Details

The Grashof number is a dimensionless number describing the ability of a parcel of fluid warmer or colder than the surrounding fluid to rise against or fall with the attractive force of gravity as follows: Gr=agL^3(Ts-Ta)/v^2 where L is the characteristic dimension, usually the vertical dimension. For reference, a cylinder's characteristic L would be its height, assuming it is standing on its end Units of L should be in metres This L should be the same L as is used for the convective coefficient calculation Ts is the surface temperature Ta is the ambient temperature v2 is the kinematic viscosity squared (calculated from airviscosity(Ta))

Author(s)

Glenn J Tattersall

References

Blaxter, K. 1989. Energy Metabolism in Animals and Man. Gates, D. M. 2003. Biophysical Ecology. Dover Publications, Mineola, New York. 611 pp.

See Also

airviscosity

Examples

# Typical values for Grashof number range from 0.016 to 4.6e+09 if Ts-Ta varies from 
# 0.1 to 30oC

L<-1
Ts<-30
Ta<-20
Grashof(L, Ts, Ta)

Description

A text file containing the palette information for use in thermal images

Description

A text file containing the palette information for use in thermal images

Description

A text file containing the palette information for use in thermal images

Description

Calculates the convective heat coefficient for an object of known dimensions, and given various physical parameters, typically only for laminar flow.

Usage

hconv(Ts=30, Ta = 20, V = 1, L = 0.1, c = NULL, n = NULL, a = NULL, b = NULL, m = NULL, 
type = "forced", shape="hcylinder")

Arguments

Details

Calculates the convection coefficient for heat transfer estimation by estimating Nusselt's number. Used in conjunction with known temperature differences in order to estimate heat transfer via convection. Gates advises to use "forced" convection coefficients down to 0.1 m/s as appropriate for very low air flow rates, rather than distinguishing between "free" and "forced" convection. Nussel's number depends on whether forced or free convection is specified. There may be some conditions (i.e. combinations of wind speeds, critical dimensions) where Nusselt's numbers are unspecified, since these values fall outside the range of Reynold's number for which estimates of convection coefficients are plausible.

Caution is advised when using hconv without considering the assumptions of convective heat exchange, and users are advised to check with Gates (2003) to see if estimates provided with this function are within the predicted range.

Value

A value corresponding to the convection coefficient, units: W/m/oC.

Author(s)

Glenn J Tattersall

References

Blaxter, 1986. Energy metabolism in animals and man. Cambridge University Press, Cambridge, UK, 340 pp.

Gates, DM. 2003. Biophysical Ecology. Dover Publications, Mineola, New York, 611 pp.

See Also

qconv

Examples

## The function is currently defined as
function (Ts=30, Ta = 20, V = 1, L = 0.1, c = NULL, n = NULL, a = NULL, b = NULL, 
    m = NULL, type = "forced", shape="hcylinder") 
{
    if (V == 0) 
        type <- "free"
    if (type == "forced" | type == "Forced") 
        Nu <- Nusseltforced(c = c, n = n, V = V, L = L, Ta = Ta, shape="hcylinder")
    if (type == "free" | type == "Free") 
        Nu <- Nusseltfree(a = a, b = b, m = m, L = L, Ts = Ts, Ta = Ta, shape="hcylinder")
    k <- airtconductivity(Ta)
    hconv <- Nu * k/L
    hconv
  }

Description

A text file containing the palette information for use in thermal images

Description

A text file containing the palette information for use in thermal images

Description

Estimates downward incoming longwave radiation (W/m2) using relationship derived from Konzelmann et al. 1994.

Usage

Ld(Ta = 20, RH = 0.5, n = 0.5)

Arguments

Details

By estimating the sky emissivity, from information on humidity and cloud cover, the incoming infrared radiation can be estimated using the Stephan-Boltzmann relationship: emissivity*Stephan Boltzmann constant * T^4. The effective atmospheric emissivity is determined from known cloud emissivity (0.97) and empirically determined clear sky emissivities.

Value

A value, vector of length one, corresponding to the incoming longwave radiation, units: W/m2.

Author(s)

Glenn J Tattersall

References

Konzelmann et al 1994. Parameterization of global and longwave incoming radiation for the Greenland ice-sheet. Global and Planetary Change. 9: 143-164.

See Also

Lw

Examples

# Returns a value in W/m2 of the estimated incoming longwave radiation  
# Example calculation:

Ta<-30
RH<-0.5
n<-0
Ld(Ta, RH, n)

Description

Returns the index locations that match vector fid within data vector. Used mostly to search for magic byte locations in a raw vector, but can be used to search any vector for locations of fid.

Usage

locate.fid(fid, vect, long = TRUE, zeroindex = TRUE)

Arguments

Details

Returns the positions within the data vector where fid is found. Do not use this function if fid is length = 1. Use which(). If length(fid)>1, the elements of fid must be adjacent and in that specific order.

Value

An object of type integer, to be used as an index subset.

Author(s)

Glenn J. Tattersall

See Also

match which

Examples

# Similar to the which or match functions in package::base, except that this returns the 
# index placement where variable fid occurs in data

## Define a vector
s<-c(2,3,42,38,88,33,55,99,32,56,22,48,1,2,3,5,6,7,8,9,10,12,20)
## Define what fid sequence to look for: i.e. what adjacent elements to look for in 
## this order
fid<-c(22,48)
## look for all instances where 22 and 48 occur together, using locate.fid
system.time(where.locate<-locate.fid(fid,s,long=FALSE, zeroindex=FALSE))
where.locate
## verify that locate.fid worked by subsetting s, using where.locate as index
s[where.locate]
system.time(where.locate<-locate.fid(fid,s,long=TRUE, zeroindex=FALSE))
s[where.locate]

## longer algorithm check
### Define a vector of 100000 random numbers from 1 to 100
s<-ceiling(runif(100000, 0, 100))
## Define what fid sequence to look for: i.e. what adjacent elements to look for in 
## this order
fid<-c(22,48)
system.time(where.locate<-locate.fid(fid,s,long=TRUE,zeroindex=FALSE))
where.locate
## verify that locate.fid worked by subsetting s, using where.locate as index
s[where.locate]

Description

Estimates upward facing ground radiation (W/m2), from the Stephan Boltzmann relationship and ground temperature

Usage

Lu(Tg = 20, Eground = 0.97)

Arguments

Details

Calculates ground radiation facing upward. Assumes ground emissivity = 0.97. Terrain emissivities vary from 0.89 (sand, snow) to 0.97 (moist soil) - Blaxter, 1986

Value

A value, vector of length one, corresponding to the longwave radiation from the ground, units: W/m2.

Author(s)

Glenn J Tattersall

References

Blaxter, 1986. Energy metabolism in animals and man. Cambridge University Press, Cambridge, UK, 340 pp.

See Also

Ld

Examples

# Estimates ground generated longwave radiation rising up.  Units W/m2.
# Example calculation:
Tg<-30
Eground<-0.97
Lu(Tg, Eground)

Description

Estimates downward facing longwave radiation (W/m2) using a relationship derived from Gabathuler et al 2001

Usage

Lw(Ta = 20, RH = 0.5, n = 0.5)

Arguments

Details

An alternative to Ld() for estimating incoming radiation by determining an offset temperature to account for the influence of atmospheric transmission loss. The incoming infrared radiation is estimated using the Stephan-Boltzmann relationship: emissivity*Stephan Boltzmann constant*T^4

Value

A value, vector of length one, corresponding to the incoming longwave radiation, units: W/m2.

Author(s)

Glenn J Tattersall

References

Gabathuler et al 2001. Parameterization of incoming longwave radiation in high mountain environments. Physical Geography 22: 99-114

See Also

Ld

Examples

# Example calculation:
Ta<-30
RH<-0.5
n<-0
Lw(Ta, RH, n)

Description

meanEveryN calculates the mean of a vectorised data set (x) at N intervals. Means are calculated by centring around every nth data point in the vector. Upon running the function, it attempts to subdivide the vector into n discrete intervals. If the vector length is not fully divisible by n, then the remainder elements are forced to NA values and the final mean calculated.

The function returns a labelled matrix, with the average index as the first column and the mean over that range of data.

Usage

meanEveryN(x, n = 2, lag = round(n/2),showsamples=FALSE)

Arguments

Details

The general purpose of this function is to assist with time based averaging a data stream typically sampled at evenly recorded time intervals common to computerised data acquisition systems. Akin to a moving average function, except that it also resamples the data.

Value

A matrix object returned

Author(s)

Glenn J. Tattersall

See Also

slopeEveryN

Examples

## Define a vector of 50 random numbers from 1 to 100
#s<-ceiling(runif(50, 0, 100))
#x<-seq(1,50,1)
## Calculate the mean value every 4th point
#s10<-meanEveryN(s,4)

#plot(x,s,type="l",col="red")
#lines(s10,col="black")

Description

A text file containing the palette information for use in thermal images

Description

A text file containing the palette information for use in thermal images

Description

A text file containing the palette information for use in thermal images

Description

A text file containing the palette information for use in thermal images

Description

A text file containing the palette information for use in thermal images

Description

Mirrors a matrix upside-down. Used in re-arranging image data for plotting properly in R.

Usage

mirror.matrix(x)

Arguments

Value

Returns a matrix

Author(s)

Glenn J Tattersall

See Also

flip.matrix rotate90.matrix rotate270.matrix rotate180.matrix

Examples

## The function is currently defined as
function (x) 
{
    xx <- as.data.frame(x)
    xx <- rev(xx)
    xx <- as.matrix(xx)
    xx
  }
  

# par(mfrow=c(2,1),mar=c(1,1,1,1))
# r<-c(1:100,rnorm(1:100)*10,1:100)
# m<-matrix(r,50)
# image(m, axes=FALSE)
# box()
# text(.5,.5,"Matrix",col="white")
# mf<-mirror.matrix(m)
# image(mf,axes=FALSE)
# box()
# text(.5,.5,"Mirror",col="white")

Description

Returns a character with leading zeros according to the total number of filenames to be created. Useful when exporting multiple images arising from imported video data stored as a matrix or dataframe. By providing a base root name, the function will then add leading zeroes ahead of the number suffix (counter variable), according to the no.digits requested (i.e. Img0001.png, Img0002.png,...Img9999.png). Best used inside a loop exporting images.

Usage

nameleadzero(filenameroot = "Img", filetype = ".png", no.digits = 5, counter = 1)

Arguments

Details

Although this returns a single character value with leading zeros, it could be used in a loop to create a new, incremented file name (i.e. Img0001.png, Img0002.png, Img0003.png,... Img9999.png), or wrapped in an apply function:

Value

Returns a character value.

Author(s)

Glenn J Tattersall

Examples

# Using for-loop 
prefix<-"Img_"
filetype<-".png"
no.digits<-2
for(i in 1:10){
  f.txt<-nameleadzero(prefix, filetype, no.digits, counter=i)
  print(f.txt)
}

# Using an apply function
x<-unlist(lapply(1:10, nameleadzero, filenameroot="Img_", filetype=".png", no.digits=2))
x

Description

Nusselt number for forced convection. Used in estimating convective heat loss. Typical values of c and n are 0.24 and 0.6, respectively. This function sets c and n to NULL to force shape calculation checks.

Usage

Nusseltforced(c = NULL, n = NULL, V = 1, L = 0.1, Ta = 20, shape="hcylinder")

Arguments

Author(s)

Glenn J Tattersall

References

Gates, DM. 2003. Biophysical Ecology. Dover Publications, Mineola, New York, 611 pp. Blaxter, K. 1989. Energy Metabolism in Animals and Man

Examples

## The function is currently defined as
function (c = NULL, n = NULL, V = 1, L = 0.1, Ta = 20, shape="hcylinder") 
{
    Nu <- c * Reynolds(V, L, Ta)^n
    Nu
  }
  
# Example
# Usually called from the hconv() or qconv() functions
V<-1
L<-0.1
Ta<-20
shape="hcylinder"

Nu<-Nusseltforced(V=V, L=L, Ta=Ta, shape=shape)

Description

Nusselt number for free convection. Used in calculating heat loss by convection.

Usage

Nusseltfree(a=NULL, b = NULL, m = NULL, L = 0.1, Ts = 25, Ta = 20, shape="hcylinder")

Arguments

Author(s)

Glenn J Tattersall

References

Blaxter, K. 1989. Energy Metabolism in Animals and Man Gates, DM. 2003. Biophysical Ecology. Dover Publications, Mineola, New York, 611 pp.

Examples

## The function is currently defined as
function (a=NULL, b = NULL, m = NULL, L = 0.1, Ts = 20, Ta = 20) 
{
    Nu <- b * (Grashof(L, Ts, Ta)*Prandtl(Ta)^a)^m
    Nu
  }

# Nusselt number for free convection
# Example calculation:

a<-1
b<-0.58
m<-0.25
L<-1
Ts<-30
Ta<-20
Nusseltfree(a,b,m,L,Ts,Ta)


# Free convection is higher when surface temperatures are elevated.  This is the effect
# that free convection predicts: greater molecular energy of air surrounding a warmer surface
# leading to air currents over top of a warm surface.

Ts<-40
Nusseltfree(a,b,m,L,Ts,Ta)

Description

Choose from among three the following colour palettes: flir, glowblow, grey120, grey10, greyred, hotiron, ironbow, medical, midgreen, midgrey, mikronprism, mikroscan, rain, and yellow.

Usage

palette.choose(colscheme)

Arguments

Details

Colscheme is a character description drawn from the following list: ("flir", "glowblow", "grey120", "grey10", "greyred", "hotiron", "ironbow", "medical", "midgreen", "midgrey", "mikronprism", "mikroscan", "rain", "yellow")

palnames<-c("flir", "glowblow", "grey120", "grey10", "greyred", "hotiron", "ironbow", "medical", "midgreen", "midgrey", "mikronprism", "mikroscan", "rainbowpal", "yellowpal")

where "flir" is palnames[1], "rain" is palnames[13]

Value

Returns a palette to be used in various graphics functions where 'col=palette' is requested. The palette vector is formatted for use as gradient fills in plotting functions.

Author(s)

Glenn J. Tattersall

Examples

###### Example #####
palnames<-c("flir", "ironbow", "mikronprism", "glowbow", "grey120", "grey10", "greyred", 
"hotiron",  "medical", "midgreen", "midgrey", "mikroscan", "yellowpal", "rainbowpal")
palnames<-as.matrix(palnames)

pals<-apply(as.matrix(palnames),1,palette.choose)
# add palnames to a list to call in image function below

par(mfrow=c(4,1),mar=c(1,0.3,1,0.3))
r<-c(1:500)
m<-matrix(r,500)

## Show palettes
image(m, axes=FALSE, col=flirpal, main="Flir Standard Palette")
image(m, axes=FALSE, col=ironbowpal, main="Ironbow Palette") 
# smaller palette for faster plotting
image(m, axes=FALSE, col=mikronprismpal, main="Mikron Prism Palette")
image(m, axes=FALSE, col=glowbowpal, main="Glowbow Palette")
image(m, axes=FALSE, col=grey120pal, main="Grey120 Palette")
image(m, axes=FALSE, col=grey10pal, main="Grey10 Palette")
image(m, axes=FALSE, col=greyredpal, main="Greyred Palette")
image(m, axes=FALSE, col=hotironpal, main="Hotiron Palette")
image(m, axes=FALSE, col=medicalpal, main="Medical Palette")
image(m, axes=FALSE, col=midgreypal, main="Midgrey Palette")
image(m, axes=FALSE, col=mikroscanpal, main="Mikroscan Palette")
image(m, axes=FALSE, col=rainbowpal, main="Rainbow Palette")
image(m, axes=FALSE, col=yellowpal, main="Yellow Palette")

# Palettes can be run in reverse
par(mfrow=c(2,1),mar=c(1,0.3,1,0.3))
image(m, axes=FALSE, col=flirpal, main="Flir Standard Palette")
image(m, axes=FALSE, col=rev(flirpal), main="Reverse Flir Standard Palette")

Description

A quick way to plot and visualise thermal image data using the fields package image.plot function.

Usage

plotTherm(bindata, templookup = NULL, w, h, minrangeset = 20, maxrangeset = 40, trans="I",
main = NULL, thermal.palette = flirpal)

Arguments

Details

This function is a simplified wrapper to call the image.plot function in the fields package. Not all options are implemented, but default ones are shown here.

Value

Provides a rasterised plot based on a vector of data from a thermal image file.

Author(s)

Glenn J Tattersall

References

Douglas Nychka, Reinhard Furrer, John Paige and Stephan Sain (2015). "fields: Tools for spatial data." doi: 10.5065/D6W957CT (URL: http://doi.org/10.5065/D6W957CT), R package version 8.10, <URL: www.image.ucar.edu/fields>.

Examples

m = 400 # grid size
C = complex( real=rep(seq(-1.8,0.6, length.out=m), each=m ), 
imag=rep(seq(-1.2,1.2, length.out=m), m ) )
C = matrix(C,m,m)

Z = 0
X = array(0, c(m,m,20))

for (k in 1:10) {
  Z = Z^2+C
  X[,,k] = exp(-abs(Z))
}

for (k in 1:10){
  x<-as.matrix(X[,,k], nrow=400)
  x[is.na(x)]<-min(x, na.rm=TRUE)
  plotTherm(x, w=400, h=400, minrangeset=min(x), maxrangeset=max(x))
}


# set w to 640 and h to 480
w<-640
h<-480
f<-system.file("extdata", "SampleSEQ.seq", package = "Thermimage")
x<-frameLocates(f)
suppressWarnings(templookup<-raw2temp(1:65535))
alldata<-unlist(lapply(x$f.start, getFrames, vidfile=f, w=w, h=h))
alldata<-matrix(alldata, nrow=w*h, byrow=FALSE)
alltemperature<-templookup[alldata]
alltemperature<-unname(matrix(alltemperature, nrow=w*h, byrow=FALSE))

# Plot 
plotTherm(alldata[,2], templookup=templookup, w=w, h=h,  minrangeset=min(alldata),
          maxrangeset=max(alldata), trans="mirror.matrix")


# Plot all frames using binary data with templookup
x<-apply(alldata, 2, plotTherm, templookup=templookup, w=w, h=h, minrangeset=20, 
maxrangeset=40, trans="mirror.matrix")

# Plot all frames using converted temperature data
x<-apply(alltemperature, 2, plotTherm, w=w, h=h, minrangeset=min(alltemperature),
         maxrangeset=max(alltemperature), thermal.palette=flirpal, trans="mirror.matrix")

# Try other palettes:
#x<-apply(alltemperature, 2, plotTherm, w=w, h=h, minrangeset=min(alltemperature),
#maxrangeset=max(alltemperature), thermal.palette=rainbowpal, trans="mirror.matrix")

#x<-apply(alltemperature, 2, plotTherm, w=w, h=h, minrangeset=min(alltemperature),
#maxrangeset=max(alltemperature), thermal.palette=midgreypal, trans="mirror.matrix")

#x<-apply(alltemperature, 2, plotTherm, w=w, h=h, minrangeset=min(alltemperature),
#maxrangeset=max(alltemperature), thermal.palette=midgreenpal, trans="mirror.matrix")

#x<-apply(alltemperature, 2, plotTherm, w=w, h=h, minrangeset=min(alltemperature),
#maxrangeset=max(alltemperature), thermal.palette=greyredpal, trans="mirror.matrix")

#x<-apply(alltemperature, 2, plotTherm, w=w, h=h, minrangeset=min(alltemperature),
#maxrangeset=max(alltemperature), thermal.palette=hotironpal, trans="mirror.matrix")

Description

Returns the Prandtl number

Usage

Prandtl(Ta = 20)

Arguments

Details

Returns the Prandlt number

Author(s)

Glenn J Tattersall

References

Blaxter, K. 1989. Energy Metabolism in Animals and Man Gates, D. M. 2003. Biophysical Ecology. Dover Publications, Mineola, New York. 611 pp.

Examples

# Example:
Ta<-30
Prandtl(Ta)

Description

Estimates the absorbed solar radiation and infrared radiation (W/m2) of an object using known physical relationships.

Usage

qabs(Ta = 20, Tg = NULL, RH = 0.5, E = 0.96, rho = 0.1, cloud = 0, SE = 100)

Arguments

Details

Total solar radiation must be supplied at this stage. The calculation here provides the worst case scenario since since no profile/angle metrics are yet taken into account. The animal could change orientation to/away from solar beam.

Author(s)

Glenn J Tattersall

References

Blaxter, 1986. Energy metabolism in animals and man. Cambridge University Press, Cambridge, UK, 340 pp.

See Also

Ld Lu Ld qrad

Examples

## The function is currently defined as
function (Ta = 25, Tg = NULL, RH = 0.5, E = 0.96, rho = 0.1, 
    cloud = 0, SE = 100) 
{
    if (length(SE) == 1) 
        SE <- rep(SE, length(Ta))
    if (is.null(Tg)) 
        Tg <- Tg(Ta, SE)
    Ld <- Ld(Ta, RH = RH, n = cloud)
    Lu <- Lu(Tg)
    IR <- E * (Lu + Ld)/2
    qabs <- (1 - rho) * SE + IR
    qabs
  }
  
# Example:
Ta<-25
Tg<-30
RH<-0.5
E<-0.96
rho<-0.1
cloud=0
SE<-100
qabs(Ta, Tg, RH, E, rho, cloud, SE)

# If Tg is unknown it can be set to NULL, and the qabs function will estimate Tg from
# an empirical relationship of Tg vs Ta and SE from the Tground() function

qabs(Ta, Tg=NULL, RH, E, rho, cloud, SE)

# For detailed examples and explanations, see:
# https://github.com/gtatters/Thermimage/blob/master/HeatTransferCalculations.md

Description

Estimates the area specific heat transfer by conduction (W/m2). Positive

Usage

qcond(Ts = 30, Tc = 20, ktiss = 0.502, x = 1)

Arguments

Details

Usually conductive heat transfer is ignored given little surface area will be in contact with the ground, but this is included for functionality.

Author(s)

Glenn J Tattersall

References

Blaxter, 1986. Energy metabolism in animals and man. Cambridge University Press, Cambridge, UK, 340 pp.

See Also

qrad qconv

Examples

## The function is currently defined as
function (Ts = 30, Tc = 20, ktiss = 0.502, x = 1) 
{
    qcond <- ktiss * (Tc - Ts)/x
    qcond
  }

Description

Estimates heat transfer by convective heat exchange, using the heat transfer coefficient estimate, surface temperature, and air temperature. Positive value = heat gain from air to object. Negative value = heat loss from object to air.

Usage

qconv(Ts = 30, Ta = 20, V = 1, L = 0.1, c = NULL, n =NULL, a=NULL, b = NULL, m = NULL, 
type = "forced", shape="hcylinder")

Arguments

Details

Estimates an area specific rate of heat transfer (W/m2), where a negative value depicts heat loss from surface to air, while positive value depicts heat gain from air to surface. Uses the gradient in temperature (Ta minus Ts) multiplied by a convection coefficient to estimate heat transfer from a surface. Designed for estimating steady state heat exchange from animal surfaces using thermal images.

Author(s)

Glenn J Tattersall

References

Blaxter, 1986. Energy metabolism in animals and man. Cambridge University Press, Cambridge, UK, 340 pp.

See Also

hconv, forcedparameters, freeparameters

Examples

## The function is currently defined as
function (Ts = 30, Ta = 20, V = 1, L = 0.1, c = NULL, n = NULL, a=NULL,
    b = NULL, m = NULL, type = "forced", shape="hcylinder") 
{
    qconv <- (Ta - Ts) * hconv(Ta = 20, V = 1, L = 0.1, c = NULL, n = NULL, a=NULL,
    b = NULL, m = NULL, type = "forced", shape="hcylinder")
    qconv
  }

# Example:
Ts<-30
Ta<-20
V<-1
L<-0.1
type="forced"
shape="hcylinder"

qconv(Ts=Ts, Ta=Ta, V=V, L=L, type=type, shape=shape)

qconv(Ts=Ts, Ta=Ta, V=V, L=L, type=type, shape="sphere")

# For detailed examples and explanations, see:
# https://github.com/gtatters/Thermimage/blob/master/HeatTransferCalculations.md

Description

Estimates heat transfer by radiation (W/m2), using the absorbed radiation estimate from qabs() minus emitted radiation from the object surface (determined from thermal image surface temperature estimates). Positive value = heat gain from environment to object. Negative value = heat loss from object to environment.

Usage

qrad(Ts = 30, Ta = 25, Tg = NULL, RH = 0.5, E = 0.96, rho = 0.1, cloud = 0, SE = 0)

Arguments

Details

Total solar radiation must be supplied at this stage. The calculation here provides the worst case scenario since since no profile/angle metrics are yet taken into account. The animal could change orientation to/away from solar beam.

Author(s)

Glenn J Tattersall

References

Blaxter, 1986. Energy metabolism in animals and man. Cambridge University Press, Cambridge, UK, 340 pp.

See Also

Ld Lu Ld qabs

Examples

## The function is currently defined as
function (Ts = 30, Ta = 25, Tg = NULL, RH = 0.5, E = 0.96, rho = 0.1, 
    cloud = 0, SE = 0) 
{
    qrad <- qabs(Ta = Ta, Tg = Tg, RH = RH, E = E, rho = rho, 
        cloud = cloud, SE = SE) - E * StephBoltz() * (Ts + 273.15)^4
    qrad
  }

# Example:
Ts<-30
Ta<-25
Tg<-28
RH<-0.5
E<-0.96
rho<-0.1
cloud<-0
SE<-100
# qrad should result in a positive gain of heat:
qrad(Ts, Ta, Tg, RH, E, rho, cloud, SE)

# if rho is elevated (i.e. doubles reflectance of solar energy), heat exchange by 
# radiation is reduced
rho<-0.2
qrad(Ts, Ta, Tg, RH, E, rho, cloud, SE)

# But if solar energy = 0, under similar conditions, qrad is negative:
SE<-0
qrad(Ts, Ta, Tg, RH, E, rho, cloud, SE)

# For detailed examples and explanations, see:
# https://github.com/gtatters/Thermimage/blob/master/HeatTransferCalculations.md

Description

A text file containing the palette information for use in thermal images

Description

A text file containing the palette information for use in thermal images

Description

Converts a raw value obtained from binary thermal image video file into estimated temperature using standard equations used in infrared thermography.

Usage

raw2temp(raw, E = 1, OD = 1, RTemp = 20, ATemp = RTemp, IRWTemp = RTemp, IRT = 1,
RH = 50, PR1 = 21106.77, PB = 1501, PF = 1, PO = -7340, PR2 = 0.012545258, 
         ATA1=0.006569, ATA2=0.01262, ATB1=-0.002276, ATB2=-0.00667, ATX=1.9)

Arguments

Details

Note: PR1, PR2, PB, PF, and PO are specific to each camera and result from the calibration at factory of the camera's Raw data signal recording from a blackbody radiation source. Sample calibration constants for three different cameras (FLIR SC660 with 24x18 degree lens, FLIR T300 with 25x19 degree lens, FLIR T300 with 2xtelephoto.

Calibration Constants by cameras: SC660, T300(25o), T300(25o with telephoto)

PR1: PlanckR1 calibration constant PB: PlanckB calibration constant PF: PlanckF calibration constant PO: PlanckO calibration constant PR2: PlanckR2 calibration constant

The calibration constants allow for the raw digital signal conversion to and from the predicted radiance of a blackbody, using the standard equation:

temperature<-PB/log(PR1/(PR2*(raw+PO))+PF)-273.15

Also used in calculations for transmission loss are the following constants:

ATA1: Atmospheric Trans Alpha 1 0.006569 ATA2: Atmospheric Trans Alpha 2 0.012620 ATB1: Atmospheric Trans Beta 1 -0.002276 ATB2: Atmospheric Trans Beta 2 -0.006670 ATX: Atmospheric Trans X 1.900000

Some files may return slightly different ATA1, ATA2, ATB1, ATB2, and ATX values. Use the flirsettings function to find out what constants are used for your files.

Value

Returns numeric value in degrees C. Can handle vector or matrix objects

Warning

Raw values need to be greater than Planck0 constant

Author(s)

Glenn J. Tattersall

References

1. http://130.15.24.88/exiftool/forum/index.php/topic,4898.60.html

2. Minkina, W. and Dudzik, S. 2009. Infrared Thermography: Errors and Uncertainties. Wiley Press, 192 pp.

See Also

temp2raw

Examples

# General Usage:
# raw2temp(raw,E,OD,RTemp,ATemp,IRWTemp,IRT,RH,PR1,PB,PF,PO,PR2,ATA1,ATA2,ATB1,ATB2,ATX)

#
# Example with all settings at default/blackbody levels:
raw2temp(18109,1,0,20,20,20,1,50,PR1=21106.77,PB=1501,PF=1,PO=-7340,PR2=0.012545258,
ATA1=0.006569, ATA2=0.01262, ATB1=-0.002276, ATB2=-0.00667, ATX=1.9)

# Example with emissivity=0.95, distance=1m, window transmission=0.96, all temperatures=20C, 
# 50 RH:

raw2temp(18109,0.95,1,20,20,20,0.96,50) 
# Note: default calibration constants for the FLIR camera will be used if you leave out the
# calibration data

# Vector example
r<-17000:25000
t1.0<-raw2temp(r,1,0,20,20,20,0.96,50)
t0.9<-raw2temp(r,0.9,0,20,20,20,0.96,50)

dev.off()
plot(r,t1.0,type="l",col="red")
lines(r,t0.9,col="black")
legend("topleft", bty = "n", c("E=1.0", "E=0.9"), lty=c(1,1), col=c("red", "black"))

# Create a templookup vector - faster calculations when working with huge binary data files
# suppressWarnings remove the NaN warning that results from the low values falling outside the 
# range of temperatures relevant

suppressWarnings(templookup<-raw2temp(raw=1:65535)) 
r<-floor(runif(10000000, 16000,25000)) # create a long vector of raw binary values

# calculate temperature using the lookup vector:
system.time(templookup[r]) # 0.109 seconds

# calculate temperature using the raw2temp function on the raw vector: 
system.time(raw2temp(r)) # 0.248 seconds

# For information on the effectiveness of the raw2temp and temp2raw
# functions at estimating temperature properly, see the following:
# https://github.com/gtatters/ThermimageCalibration

Description

Reads an image from a FLIR JPG file into an integer matrix, w pixels wide x h pixels high, depending on image size.

Usage

readflirJPG(imagefile, exiftoolpath = "installed", headerindex=1)

Arguments

Details

Only tested on a select number of FLIR JPGs. Usage depends on functionality provided by Exiftool. At present this function first makes use of readBin to read in thermal image jpgs and searches for the magic start byte sequence ("54", "49", "46", "46","49", "49") for TIFF type images or ("89", "50", "4e", "47", "0d", "0a", "1a", "0a") for PNG type images, and then uses the readTIFF or readPNG functions to load into R.

Exiftool should install on most operating systems. Consult with http://www.sno.phy.queensu.ca/~phil/exiftool/ for information on installing Exiftool. If trouble installing, download Exiftool and set exiftoolpath to the custom folder location. To test if the custom path to Exiftool will work on your OS, try your own system or system2 call: system2("/custompath/exiftool") to see if you get an error or not.

v 2.2.3: updated to fix a problem calling shell commands requiring folder write access on a windows OS (thanks to John Al-Alawneh)

Value

Returns a matrix of integer values, corresponding the calibrated raw thermal image radiance values. Can be converted to temperature estimates using the raw2temp() function.

Note

Loading image files and manipulating them in R is slow. Consider using command line tools like exiftool, imagemagick, and ffmpeg to convert the files into a format to analyse in ImageJ, where more powerful plug-ins can be accessed.

Alternatively, convertflirjpg and convertflirvid functions are wrappers that will call command line tools and convert flir files in the shell environment.

Author(s)

Glenn J Tattersall

References

1. Exiftool Command line tool: http://www.sno.phy.queensu.ca/~phil/exiftool/

2. Simon Urbanek (2013). tiff: Read and write TIFF images. R package version 0.1-5. https://CRAN.R-project.org/package=tiff

3. Simon Urbanek (2013). png: Read and write PNG images. R package version 0.1-7. https://CRAN.R-project.org/package=png

See Also

temp2raw raw2temp convertflirJPG convertflirVID

Examples

## Not run: 
## Example using the flirsettings and readflirjpg functions

library(Thermimage)
## Sample flir jpg included with Thermimage package:

imagefile<-paste0(system.file("extdata/IR_2412.jpg", package="Thermimage"))

## Extract meta-tags from thermal image file ##
cams<-flirsettings(imagefile, exiftool="installed", camvals="")
cams

## Set variables for calculation of temperature values from raw A/D sensor data  ####
Emissivity<-cams$Info$Emissivity      # Image Saved Emissivity - should be ~0.95 or 0.96
ObjectEmissivity<-0.96                # Object Emissivity - should be ~0.95 or 0.96
dateOriginal<-cams$Dates$DateTimeOriginal
dateModif<-   cams$Dates$FileModificationDateTime
PlanckR1<-    cams$Info$PlanckR1                      # Planck R1 constant for camera  
PlanckB<-     cams$Info$PlanckB                       # Planck B constant for camera  
PlanckF<-     cams$Info$PlanckF                       # Planck F constant for camera
PlanckO<-     cams$Info$PlanckO                       # Planck O constant for camera
PlanckR2<-    cams$Info$PlanckR2                      # Planck R2 constant for camera
OD<-          cams$Info$ObjectDistance                # object distance in metres
FD<-          cams$Info$FocusDistance                 # focus distance in metres
ReflT<-       cams$Info$ReflectedApparentTemperature  # Reflected apparent temperature
AtmosT<-      cams$Info$AtmosphericTemperature        # Atmospheric temperature
IRWinT<-      cams$Info$IRWindowTemperature           # IR Window Temperature
IRWinTran<-   cams$Info$IRWindowTransmission          # IR Window transparency
RH<-          cams$Info$RelativeHumidity              # Relative Humidity
h<-           cams$Info$RawThermalImageHeight         # sensor height (i.e. image height)
w<-           cams$Info$RawThermalImageWidth          # sensor width (i.e. image width)

## Import image from flir jpg to obtain binary data
img<-readflirJPG(imagefile)

## Rotate image before plotting
imgr<-rotate270.matrix(img)

## Plot initial image of raw binary data
library(fields)
image.plot(imgr, useRaster=TRUE, col=ironbowpal)

## Convert binary data to temperature

## Consider whether you should change any of the following: 
## ObjectEmissivity, OD, RH, ReflT, AtmosT, IRWinT, IRWinTran

temperature<-raw2temp(imgr,ObjectEmissivity,OD,ReflT,AtmosT,IRWinT,IRWinTran,RH,
                      PlanckR1,PlanckB,PlanckF,PlanckO,PlanckR2)
colnames(temperature)<-NULL
rownames(temperature)<-NULL

## Plot temperature image using fields package
t<-temperature
image.plot(t, asp=h/w, bty="n", useRaster=TRUE, xaxt="n", yaxt="n", col=ironbowpal)

## Plot temperature image using ggplot2
library(ggplot2)
library(reshape2)
d<-melt(temperature)

p<-ggplot(d, aes(Var1, Var2))+
  geom_raster(aes(fill=value))+coord_fixed()+
  scale_fill_gradientn(colours=ironbowpal)+
  theme_void()+
  theme(legend.key.height=unit(2, "cm"), legend.key.width=unit(0.5, "cm"))
p


## Export Temperature Data to CSV file
## Must rotate image 90 degrees before exporting
## This csv file can be imported into imageJ (File-Import-Text Image) for open source image
## analysis options of accurate thermal image data.  If you have many csv files, consider
## writing a macro, see:
## http://imagej.1557.x6.nabble.com/open-text-image-sequence-td4999149.html


f.temperature<-"IR_2412.csv"
write.csv(rotate90.matrix(temperature), f.temperature, row.names=FALSE)


## End(Not run)

## See also https://github.com/gtatters/Thermimage/README.md

Description

Calculates the Reynolds number, a unitless measure.

Usage

Reynolds(V, L, v)

Arguments

Author(s)

Glenn J Tattersall

References

Blaxter, K. 1989. Energy Metabolism in Animals and Man Gates, D. M. 2003. Biophysical Ecology. Dover Publications, Mineola, New York. 611 pp.

Examples

## The function is currently defined as
function (V, L, v) 
{
  v<-airviscosity(Ta)
  Re<-V*L/v
  }

# Typical values for Reynolds numbers range from 6.6 to 6.6e+5 

# Example calculation:
V<-1
L<-1
Ta<-20
v<-airviscosity(Ta)
Reynolds(V, L, v)

Description

Rotate a matrix by 180 degrees. Used for adjusting image plotting in R.

Usage

rotate180.matrix(x)

Arguments

Value

Returns a matrix

Author(s)

Glenn J Tattersall

References

1. http://www.inside-r.org/packages/cran/RSEIS/docs/mirror.matrix

2. Based on similar code in package <RSEIS>

See Also

flip.matrix mirror.matrix rotate90.matrix rotate270.matrix

Examples

## The function is currently defined as
function (x) 
{
    xx <- rev(x)
    dim(xx) <- dim(x)
    xx
  }
  
  
# set.seed(5)
# par(mfrow=c(1,2),mar=c(1,1,1,1))
# r<-c(1:100,rnorm(1:100)*10,1:100)
# m<-matrix(r,50)
# image(m, axes=FALSE)
# box()
# text(.5,.5,"Matrix")
# mf<-rotate180.matrix(m)
# image(mf,axes=FALSE)
# box()
# text(.5,.5,"Rotate180",col="white")

Description

Rotate a matrix by 270 degrees counterclockwise (or 90 degree clockwise). Used for adjusting image plotting in R.

Usage

rotate270.matrix(x)

Arguments

Value

Returns a matrix

Author(s)

Glenn J Tattersall

References

1. http://www.inside-r.org/packages/cran/RSEIS/docs/mirror.matrix

2. Based on similar code in package <RSEIS>

See Also

flip.matrix mirror.matrix rotate90.matrix rotate180.matrix

Examples

## The function is currently defined as
function (x) 
{
    mirror.matrix(t(x))
  }
  
  

 set.seed(5)
 par(mfrow=c(1,2),mar=c(1,1,1,1))
 r<-c(1:100,rnorm(1:100)*10,1:100)
 m<-matrix(r,50)
 image(m, axes=FALSE)
 box()
 text(.5,.5,"Matrix",col="white")
 mf<-rotate270.matrix(m)
 image(mf,axes=FALSE)
 box()
 text(.5,.5,"Rotate270",col="white")

Description

Rotate a matrix by 90 degrees counterclockwise (270 degrees clockwise). Used for adjusting image plotting in R.

Usage

rotate90.matrix(x)

Arguments

Value

Returns a matrix.

Author(s)

Glenn J. Tattersall

References

1. http://www.inside-r.org/packages/cran/RSEIS/docs/mirror.matrix

2. Based on similar code in package <RSEIS>

See Also

flip.matrix mirror.matrix rotate270.matrix rotate180.matrix

Examples

## The function is currently defined as
function (x) 
{
    t(mirror.matrix(x))
  }
  
set.seed(5)
par(mfrow=c(1,2),mar=c(1,1,1,1))
r<-c(1:100,rnorm(1:100)*10,1:100)
m<-matrix(r,50)
image(m, axes=FALSE)
box()
text(.5,.5,"Matrix",col="white")
mf<-rotate90.matrix(m)
image(mf,axes=FALSE)
box()
text(.5,.5,"Rotate90",col="white")

Description

A sample thermal image to demonstrate thermal colour palette use.

Usage

data("samp.image")

Format

A sample thermal image to demonstrate thermal colour palette use. The format is: num [1:480, 1:640] 23.2 23.2 23.4 23.3 23.3 ...

Examples

###### Example #####
palnames<-c("flir", "ironbow", "mikronprism", "glowbow", "grey120", "grey10", "greyred", 
"hotiron",  "medical", "midgreen", "midgrey", "mikroscan", "yellowpal", "rainbowpal")

m<-rotate90.matrix(samp.image)
par(mfrow=c(2,1),mar=c(0.3,2,1,2))

## Show palettes
image(m, axes=FALSE, useRaster=TRUE, col=flirpal, main="Flir Standard Palette")
image(m, axes=FALSE, useRaster=TRUE, col=ironbowpal, main="Ironbow Palette") 
# smaller palette for faster plotting
image(m, axes=FALSE, useRaster=TRUE, col=mikronprismpal, main="Mikron Prism Palette")
image(m, axes=FALSE, useRaster=TRUE, col=glowbowpal, main="Glowbow Palette")
image(m, axes=FALSE, useRaster=TRUE, col=grey120pal, main="Grey120 Palette")
image(m, axes=FALSE, useRaster=TRUE, col=grey10pal, main="Grey10 Palette")
image(m, axes=FALSE, useRaster=TRUE, col=greyredpal, main="Greyred Palette")
image(m, axes=FALSE, useRaster=TRUE, col=hotironpal, main="Hotiron Palette")
image(m, axes=FALSE, useRaster=TRUE, col=medicalpal, main="Medical Palette")
image(m, axes=FALSE, useRaster=TRUE, col=midgreypal, main="Midgrey Palette")
image(m, axes=FALSE, useRaster=TRUE, col=mikroscanpal, main="Mikroscan Palette")
image(m, axes=FALSE, useRaster=TRUE, col=rainbowpal, main="Rainbow Palette")
image(m, axes=FALSE, useRaster=TRUE, col=yellowpal, main="Yellow Palette")

Description

Returns the slope from linear regression with x values as equally spaced 1:length

Usage

slopebypoint(data)

Arguments

Details

Returns the slope (i.e. localised tangent) from linear regression with x values as equally spaced 1:length. The usefulness of this function is to reduce a time series type of data collected at equal time intervals.

N=number of data points over which to calculate the slope.

Value

An object of type numeric.

Author(s)

Glenn J. Tattersall

See Also

lm

Examples

## Define a vector of 50 random numbers from 1 to 100
y<-ceiling(runif(50, 0, 100))
# Calculate the slope with respect to the index values (i.e. 1 to 50)
# instead of an x axis, this will provide a slope value of y vs. index
s<-slopebypoint(y)
s

# same as if typing: 
lm(y~seq(0,length(y)-1,1))

Description

slopeEveryN calculates the slope of a vectorised data set (x) at N intervals. Slopes are calculated using the lm() function centred around every nth data point in the vector. Upon running the function, it attempts to subdivide the vector into n discrete intervals. If the vector length is not fully divisible by n, then the remainder elements are forced to NA values and the final slope calculated.

The function returns a labelled matrix, with the average index as the first column and the slope over that range of data. Units for slope then are technically in un

Usage

slopeEveryN(x, n = 2, lag = round(n/2))

Arguments

Details

The general purpose of this function is to provide a moving average of a data stream typically sampled at evenly recorded time intervals common computerised data acquisition systems. Akin to a moving average function, except that it also resamples the data.

Value

A matrix object returned

Author(s)

Glenn J. Tattersall

See Also

slopebypoint

Examples

## Define a vector of 50 random numbers from 1 to 100
s<-ceiling(runif(50, 0, 100))
x<-seq(1,50,1)
# Calculate the slope value every 4th point
s10<-slopeEveryN(s,4)

plot(x,s,type="l",col="red")
lines(s10,col="black")

Description

The Stephan Boltzman constant. Units: W/m^2/K^4

Usage

StephBoltz()

Author(s)

Glenn J Tattersall

Examples

## The function is currently defined as
function () 
{
    s <- 5.67e-08
    s
  }
  
# Example
# This is simply the Stephan Boltzmann constant, saves having to remember the exact value
# and it allows easier coding.  To call it, type:

StephBoltz()

Description

Operative temperature (degrees Celsius) is a measure of the effective temperature an object/animal will be given a specific radiative and convective environment. Basal heat production and evaporative heat loss are assumed to balance each other out.

Usage

Te(Ts=30, Ta=25, Tg=NULL, RH=0.5, E=0.96, rho=0.1, cloud=0, SE=0, V=1, 
L=0.1, c=NULL, n=NULL, a=NULL, b=NULL, m=NULL, type="forced", shape="hcylinder")

Arguments

Details

Estimates operative temperature according to calculations in Gates (2003) and Angiletta ()

Author(s)

Glenn J Tattersall

References

Angiletta, M. J. 2009. Thermal Adaptation: A Theoretical and Empirical Synthesis. Oxford University Press, Oxford, UK, 304 pp. Gates, D.M. 2003. Biophysical Ecology. Courier Corporation, 656 pp.

See Also

qabs hconv

Examples

# Example

Ts<-40
Ta<-30
SE<-seq(0,1500,100)
Toperative<-NULL
for(rho in seq(0, 1, 0.1)){
  temp<-Te(Ts=Ts, Ta=Ta, Tg=NULL, RH=0.5, E=0.96, rho=rho, cloud=1, SE=SE, V=0.1, 
           L=0.1, type="free", shape="hcylinder")
  Toperative<-cbind(Toperative, temp)
}
Toperative<-data.frame(SE=seq(0,1500,100), Toperative)
colnames(Toperative)<-c("SE", seq(0,1,0.1))
matplot(Toperative$SE, Toperative[,-1], ylim=c(30, 50), type="l", xlim=c(0,1000),
        ylab="Operative Temperature (C)", xlab="Solar Radiation (W/m2)", lty=1,
        col=flirpal[rev(seq(1,380,35))])

# For detailed examples and explanations, see:
# https://github.com/gtatters/Thermimage/blob/master/HeatTransferCalculations.md

Description

Inverse of the function raw2temp. Typically used when incorrect settings were used during thermal imaging analysis, and the raw values need to be extracted in order to re-calculate temperature using raw2temp. Parameters under which the temperatures were estimated should be known, since the conversion to raw will take those into account.

Usage

temp2raw(temp, E = 1, OD = 1, RTemp = 20, ATemp = RTemp, IRWTemp = RTemp, IRT = 1, 
RH = 50, PR1 = 21106.77, PB = 1501, PF = 1, PO = -7340, PR2 = 0.012545258,
  ATA1=0.006569, ATA2=0.01262, ATB1=-0.002276, ATB2=-0.00667, ATX=1.9)

Arguments

Details

Note: PR1, PR2, PB, PF, and PO are specific to each camera and result from the calibration at factory of the camera's Raw data signal recording from a blackbody radiation source. Sample calibration constants for three different cameras (FLIR SC660 with 24x18 degree lens, FLIR T300 with 25x19 degree lens, FLIR T300 with 2xtelephoto.

Calibration Constants by cameras: SC660, T300(25o), T300(25o with telephoto)

PR1: PlanckR1 calibration constant PB: PlanckB calibration constant PF: PlanckF calibration constant PO: PlanckO calibration constant PR2: PlanckR2 calibration constant

The calibration constants allow for the raw digital signal conversion to and from the predicted radiance of a blackbody, using the standard equation:

temperature<-PB/log(PR1/(PR2*(raw+PO))+PF)-273.15

Also used in calculations for transmission loss are the following constants: ATA1: Atmospheric Trans Alpha 1 0.006569 ATA2: Atmospheric Trans Alpha 2 0.012620 ATB1: Atmospheric Trans Beta 1 -0.002276 ATB2: Atmospheric Trans Beta 2 -0.006670 ATX: Atmospheric Trans X 1.900000

Some files may return slightly different ATA1, ATA2, ATB1, ATB2, and ATX values. Use the flirsettings function to find out what constants are used for your files.

Value

Returns numeric value. Can handle vector or matrix objects.

Author(s)

Glenn J. Tattersall

References

1. http://130.15.24.88/exiftool/forum/index.php/topic,4898.60.html

2. Minkina, W. and Dudzik, S. 2009. Infrared Thermography: Errors and Uncertainties. Wiley Press, 192 pp.

See Also

raw2temp

Examples

# General Usage:
# temp2raw(temp,E,OD,RTemp,ATemp,IRWTemp,IRT,RH,PR1,PB,PF,PO,PR2)

# Example with all settings at default/blackbody levels:
temp2raw(23,1,0,20,20,20,1,50,PR1=21106.77,PB=1501,PF=1,PO=-7340,PR2=0.012545258)

# Example with emissivity=0.95, distance=1m, window transmission=0.96, all temperatures=20C,
# 50 RH:

temp2raw(23,0.95,1,20,20,20,0.96,50) 
# Note: default calibration constants for my FLIR camera will be used if you leave out the
# calibration data

t<-10:50
r1.0<-temp2raw(t,1,0,20,20,20,0.96,50)
r0.9<-temp2raw(t,0.9,0,20,20,20,0.96,50)

dev.off()
plot(t,r1.0,type="l",col="red")
lines(t,r0.9,col="black")
legend("topleft", bty = "n", c("E=1.0", "E=0.9"), lty=c(1,1), col=c("red", "black"))

# For information on the effectiveness of the raw2temp and temp2raw 
# functions at estimating temperature properly, see the following:
# https://github.com/gtatters/ThermimageCalibration

Description

Estimates equivalent black-body temperature of an object. Analagous to other measures of operative temperature

Usage

Teq(Ts = 30, Ta = 25, Tg = NULL, RH = 0.5, E = 0.96, rho = 0.1, cloud = 0, SE = 0, V = 1, 
L = 0.1, type = "forced")

Arguments

Author(s)

Glenn J Tattersall

References

Mahoney, S.A. and King, J. R. (1977). The use of the equivalent black-body temperautre in the thermal energetics of small birds. J Thermal Biol. 2: 115-120

Examples

## The function is currently defined as
function (Ts = 30, Ta = 25, Tg = NULL, RH = 0.5, E = 0.96, rho = 0.1, 
    cloud = 0, SE = 0, V = 1, L = 0.1, type = "forced") 
{
    if (type == "forced") 
        k <- 0.7 * 310
    if (type == "free") 
        k <- 310
    rr <- airdensity(Ta) * airspecificheat(Ta)/(4 * E * StephBoltz() * 
        (Ta + 273.15)^3)
    ra <- k * (L/V)^0.5
    re <- 1/(1/ra + 1/rr)
    Rni <- qabs(Ta = Ta, Tg = Tg, RH = RH, E = E, rho = rho, 
        cloud = cloud, SE = SE) - StephBoltz() * E * (Ta + 273.15)^4
    Teq <- Ta + Rni * re/(airdensity(Ta) * airspecificheat(Ta))
    Teq
  }
  
# For detailed examples and explanations, see:
# https://github.com/gtatters/Thermimage/blob/master/HeatTransferCalculations.md

Description

Estimates ground temperature from ambient temperature and solar radiation.

Usage

Tground(Ta = 20, SE = 100)

Arguments

Details

If ground temperature is not measured, but air temperature and solar energy are provided, ground temperature can be estimated from empirical relationships. Ground temperature is used in obtain incoming longwave radiation from the ground.

Value

Returns a vector of one, with an estimate of ground temperature.

Author(s)

Glenn J Tattersall

References

Bartlett et al. 2006. A decade of ground-air temperature tracking at emigrant pass observatory, Utah. Journal of Climate. 19: 3722-3731.

Examples

# Example:
Ta<-25
SE<-200
Tground(Ta, SE)

# For detailed examples and explanations, see:
# https://github.com/gtatters/Thermimage/blob/master/HeatTransferCalculations.md

Description

Provides typical summary data (min, max, mean, sd, median) of a vector of raw binary thermal encoded data. If templookup is not provided, the summary info is conducted on the data provided. If a templookup vector is provided (see Examples in raw2temp function), the dat values are converted to temperature before summary information is extracted.

Usage

thermsum(dat, templookup = NULL)

Arguments

Details

A simple summary function for thermal imaging data to allow for extraction of basic statistical data from a thermal image dataset. If dat is supplied as an integer vector of raw binary values, then templookup should be supplied to use as an indexing function.

Using raw2temp(1:65535) will produce a vector of temperatures that correspond to the indexed integers 1:65535. This method of calculation can be faster on large video files. The default settings for raw2temp() will not be appropriate, and all camera settings should be used according to calibration constants.

If dat is supplied as a vector of temperatures, then templookup must be left blank or NULL as the default. Summary information will be calculated on the dat variable assuming it is properly calibrated temperature values.

As written, this is a vectorised function, so will only calculate summary on the vector provided. To perform thermal summaries on multiple frames from the raw binary video data, use a for-loop (usually slow) or the apply function to process (faster processing) or parallel apply functions (best).

Value

Returns a named vector: Mintemp, Maxtemp, Meantemp, SDtemp, and Mediantemp

Warning

This function simply calculates summary data, and does not detect objects in the image frame. Use only as rapid way to extract thermal information. This is not a replacement for doing analysis by hand, and may only be useful for objects that are stationary and remain within the image frame over time.

Author(s)

Glenn J Tattersall

See Also

raw2temp, thermsumcent

Examples

# set w to 640 and h to 480
w<-640
h<-480
f<-system.file("extdata", "SampleSEQ.seq", package = "Thermimage")
x<-frameLocates(f, w=w, h=h)
suppressWarnings(templookup<-raw2temp(1:65535))
alldata<-unlist(lapply(x$f.start, getFrames, vidfile=f, w=w, h=h))
alldata<-matrix(alldata, nrow=w*h, byrow=TRUE)

# Summary on one image or frame of data
thermsum(alldata[,1], templookup)

# Summary on multi-frame seq file
tsum<-data.frame(t(apply(alldata, 2, thermsum, templookup)))
tsum

# Randomly generated data
alldata<-floor(runif(w*h*10, 17000, 25000))
alldata<-matrix(alldata, nrow=w*h)

# depending on the size of alldata, directly calculating temperature can slow down processing
# For a 10 frame file:
system.time(alltemperature<-raw2temp(alldata))

# But summary calculations using raw binary with lookup are slightly slower than
# using numeric temperatures:

# Perform calculations on the raw binary but supply the templookup vector
system.time(tsum<-data.frame(t(apply(alldata, 2, thermsum, templookup))))

# Perform calculations on the converted temperature values
system.time(tsum<-data.frame(t(apply(alltemperature, 2, thermsum))))
tsum

Description

Similarly to the thermsum except this provides thermal summary data on a central region of interest, commonly used in thermal imaging. The size of the region is a rectangular region corresponding to a fraction of the total image area set by boxsize.

Usage

thermsumcent(dat, templookup = NULL, w = 640, h = 480, boxsize = 0.05)